Fusion VIO: An Extendable Sensor Fusion Framework for Unmaned Vehicles

Abstract

Real-time perception based on the simultaneous localization and mapping (SLAM) technology is of immense application potential in the industry filed. In this work, we provide an extended Kalman filter (EKF) based visual SLAM framework named Fusion VIO, which contains HDR imaging system, hardware synchronization, object detection, filter based sensor fusion, and semantic fusion. Notably, Fusion VIO well fuses loop closure information, which takes effects in solving the error problem during operation of the robot and improving the robustness of the system. Besides, experimental validation shows that Fusion VIO not only maintains high accuracy but also cost one-third resources compared to the common VINS fusion. Our work demonstrates a novel sensor fusion framework for unmaned vehicles.